{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 精准度-召回率曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "digits = datasets.load_digits()\n",
    "X = digits.data\n",
    "y = digits.target.copy()\n",
    "\n",
    "y[digits.target==9] = 1\n",
    "y[digits.target!=9] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=666)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "log_reg = LogisticRegression()\n",
    "log_reg.fit(X_train, y_train)\n",
    "decision_scores = log_reg.decision_function(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "\n",
    "# decision_score的最小值和最大值之间取各种threshold,步长0.1\n",
    "precisions = []\n",
    "recalls = []\n",
    "thresholds = np.arange(np.min(decision_scores), np.max(decision_scores), 0.1)\n",
    "for threshold in thresholds:\n",
    "    y_predict = np.array(decision_scores >= threshold, dtype='int')\n",
    "    precisions.append(precision_score(y_test, y_predict))\n",
    "    recalls.append(recall_score(y_test, y_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot(thresholds, precisions, label=\"precision\")\n",
    "plt.plot(thresholds, recalls, label=\"recall\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Precision-Recall 曲线\n",
    "横轴precision，纵轴recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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XpIow0CWpIgx0SaoIA12SKsJAl6SKMNAlqSIMdEmqCANdkirCQJekijDQJakiDHRJqggDXZIqwkCXpIow0CWpIgx0SaoIA12SKsJAl6SKMNAlqSIMdEmqCANdkirCQJekijDQJakiCgV6RKyMiOciojsibhtl/2cjYmNEPB0RD0TEWfUvVZI0lnEDPSJagTXAtcAFwE0RccERzZ4EOjPzncD3gK/Uu1BJ0tiKjNAvA7oz84XMHADuAVaPbJCZD2bm/uHNx4Al9S1TkjSeIoG+GNg8YnvL8HtHczPwg9F2RMQtEdEVEV09PT3Fq5QkjatIoMco7+WoDSM+CnQCd462PzPXZmZnZna2t7cXr1KSNK4ZBdpsAZaO2F4CbDuyUURcA3wBuCozD9anPElSUUVG6I8DyyPi7IhoA24E1o1sEBErgK8DqzJzR/3LlCSNZ9xAz8xB4FbgfuBZ4N7M3BARd0TEquFmdwInAX8dET+LiHVHOZwkaYoUmXIhM9cD64947/YRr6+pc12SpAnyTlFJqggDXZIqwkCXpIow0CWpIgx0SaoIA12SKsJAl6SKMNAlqSIMdEmqCANdkirCQJekijDQJakiDHRJqggDXZIqwkCXpIow0CWpIgx0SaoIA12SKsJAl6SKMNAlqSIMdEmqCANdkirCQJekijDQJakiDHRJqggDXZIqwkCXpIow0CWpIgx0SaoIA12SKsJAl6RpMqu1lesuPp1lp8yZkuMXCvSIWBkRz0VEd0TcNsr+WRHx3eH9P4mIjnoXKknNbt6cmdz1kUt573ntU3L8cQM9IlqBNcC1wAXATRFxwRHNbgZ2Zea5wB8Df1DvQiVJYysyQr8M6M7MFzJzALgHWH1Em9XAt4Zffw+4OiKifmVKksZTJNAXA5tHbG8Zfm/UNpk5CPQBC488UETcEhFdEdHV09NzbBVLkkZVJNBHG2nnMbQhM9dmZmdmdra3T80ckiQdr4oE+hZg6YjtJcC2o7WJiBnAPKC3HgVKkoopEuiPA8sj4uyIaANuBNYd0WYd8PHh19cDP8zMt43QJUlTZ8Z4DTJzMCJuBe4HWoFvZOaGiLgD6MrMdcBfAH8VEd3URuY3TmXRkqS3GzfQATJzPbD+iPduH/G6H7ihvqVJkiYiypoZiYge4KVSTl4/i4DXyi6igdgfb7EvDmd/HG4y/XFWZo56VUlpgV4FEdGVmZ1l19Eo7I+32BeHsz8ON1X94bNcJKkiDHRJqggDfXLWll1Ag7E/3mJfHM7+ONyU9Idz6JJUEY7QJakiDHRJqggDvYACC3x8NiI2RsTTEfFARJxVRp3TYby+GNHu+ojIiKj0pWpF+iMiPjT8+7EhIr493TVOpwJ/V5ZFxIMR8eTw35fryqhzOkTENyJiR0Q8c5T9ERFfHe6rpyPikkmfNDP9GeOH2uMO/h/wS0Ab8BRwwRFtfhWYM/z608B3y667rL4YbjcXeBh4DOgsu+6SfzeWA08CC4a3Ty277pL7Yy3w6eHXFwCbyq57CvvjvcAlwDNH2X8d8ANqT6u9HPjJZM/pCH184y7wkZkPZub+4c3HqD2RsoqKLHYC8CXgK0D/dBZXgiL98SlgTWbuAsjMHdNc43Qq0h8JnDz8eh5vf3JrZWTmw4z91NnVwN1Z8xgwPyLOmMw5DfTxFVngY6Sbqf2rW0Xj9kVErACWZuZ901lYSYr8bpwHnBcRj0TEYxGxctqqm35F+uOLwEcjYgu150N9ZnpKa0gTzZZxFXo413Gu0OIdABHxUaATuGpKKyrPmH0RES3U1pT9xHQVVLIivxszqE27vI/a/9z+KSIuyszdU1xbGYr0x03ANzPzDyPiCmpPab0oM4emvryGUzhbinKEPr4iC3wQEdcAXwBWZebBaaptuo3XF3OBi4CHImITtXnBdRX+YrTo4i9/m5lvZOaLwHPUAr6KivTHzcC9AJn5Y2A2tQdVHY8KZctEGOjjG3eBj+Fphq9TC/Mqz5GO2ReZ2ZeZizKzIzM7qH2fsCozu8opd8oVWfzl+9S+NCciFlGbgnlhWqucPkX642XgaoCIOJ9aoB+vCwyvAz42fLXL5UBfZm6fzAGdchlHFlvg407gJOCvIwLg5cxcVVrRU6RgXxw3CvbH/cD7I2IjcAj4fGbuLK/qqVOwPz4H/FlE/Ftq0wufyOFLPqomIr5Dbapt0fB3Br8HzATIzD+l9h3CdUA3sB/45KTPWdG+lKTjjlMuklQRBrokVYSBLkkVYaBLUkUY6JJUEQa6JFWEgS5JFfH/ARI4y5AOhKGuAAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot(precisions, recalls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### scikit-learn中的Precision-Recall曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# sklearn中有precision-recall曲线\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "precisions, recalls, thresholds = precision_recall_curve(y_test, decision_scores)\n",
    "# sklearn会自动定义步长\n",
    "# sklearn中会去掉一部分数据，只取比较重要的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[-16.9108351  -16.74188106 -16.72073864 -16.69277014 -16.64524478\n -16.63673577 -16.58845322 -16.54767755 -16.51448278 -16.45783234\n -16.45722466 -16.40358049 -16.38036199 -16.33234463 -16.32354322\n -16.12112652 -16.01327979 -15.88388534 -15.74971561 -15.73740616\n -15.69914566 -15.67638209 -15.59159291 -15.54212475 -15.52835977\n -15.21275749 -15.02586414 -15.016642   -14.95621898 -14.64884939\n -14.51722993 -14.40452232 -14.40137403 -14.24992352 -13.74958126\n -13.49528663 -13.48744533 -13.26705364 -13.24528777 -13.23498169\n -13.10296371 -13.02648525 -12.83311007 -12.65248251 -12.22118319\n -11.92685225 -11.92246583 -11.80430618 -11.78584772 -11.67129106\n -11.66879852 -11.56719436 -11.54702006 -11.46462152 -11.33835475\n -11.28643093 -11.22421785 -11.19626544 -11.02298233 -10.32361663\n -10.3065272  -10.13039635  -9.95503959  -9.85938813  -9.68738007\n  -9.66915657  -9.40788002  -9.26258151  -9.14577037  -9.01260419\n  -8.55333406  -8.3662785   -8.15816583  -8.08230001  -7.92363237\n  -7.67802146  -7.65927585  -7.61039457  -7.38109641  -7.23753396\n  -6.67666874  -6.67193926  -6.63889956  -6.27810027  -6.16117272\n  -6.14362682  -6.05120072  -5.77384617  -5.7110051   -5.70727488\n  -5.60305826  -5.22476924  -4.96737222  -4.66508991  -4.63766499\n  -4.60961487  -4.52459456  -4.45238648  -4.10861153  -3.5482486\n  -3.14083977  -2.84895765  -2.83181403  -2.45074492  -2.10091732\n  -1.6245673   -1.22419553  -1.11043103  -0.89614577  -0.82911845\n   0.9932652    1.53214815   1.5488168    2.37046267   2.56637871\n   2.77443423   3.55513266   3.72490794   3.77499093   4.60193605\n   4.62655592   4.75649274   4.98058731   5.31040699   5.56731178\n   5.92692281   6.22096769   6.3459115    7.00445449   7.49846968\n   7.73220166   7.7435527    8.54295682   8.96514447   9.45735825\n   9.8575588   10.41999739  11.02224931  11.52909745  12.01777967\n  12.02203906  12.2484809   14.1234828   14.44408478  14.69557676\n  15.22942777  19.69960124  19.97514257]\n"
    }
   ],
   "source": [
    "print(thresholds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(149,)"
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "precisions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(149,)"
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "recalls.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(148,)"
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "thresholds.shape\n",
    "# 最后一个的精确率为1，召回率为0，此时threshold不存在，所以threshold的shape会比precision和recall小1，sklearn定义"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 需要抛去最后一个值\n",
    "plt.plot(thresholds, precisions[:-1])\n",
    "plt.plot(thresholds, recalls[:-1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot(precisions, recalls)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6.8 64-bit ('anaconda': conda)",
   "language": "python",
   "name": "python36864bitanacondaconda085db2e8b14649f4b32196068f7124e9"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}